Incorporating canonical discriminant attributes in classification learning

This paper describes a method for incorporating canonical discriminant attributes in classification machine learning. Though decision trees and rules have semantic appeal when building expert systems, the merits of discriminant analysis are well documented. For data sets on which discriminant analysis obtains significantly better predictive accuracy than symbolic machine learning, the incorporation of canonical discriminant attributes can benefit machine learning. The process starts by applying canonical discriminant analysis to the training set. The canonical discriminant attributes are included as additional attributes. The expanded data set is then subjected to machine learning. This enables linear combinations of numeric attributes to be incorporated in the classifiers that are learnt. Evaluation on the data sets on which discriminant analysis performs better than most machine learning systems, such as the Iris flowers and Waveform data sets, shows that incorporating the power of discriminant analysis in machine classification learning can significantly improve the predictive accuracy and reduce the complexity of classifiers induced by machine learning systems.